A.L-5938

Bachelor

Lab Technician

advanced statistical analysis using R and Python animal behavior Animal Handling Assay Development Automated DNA Extraction Behavioral Assays Biological Data Analysis Cell Culture Confocal Microscopy Confocal/epifluorescence imaging cryostat sectioning DNA and RNA isolation and quantification ELISA Fluorescence Microscopy Graphpad Prism imaging Immunoassays Immunohistochemistry In-vitro techniques: Mammalian cell/tissue culture Luminex PCR

Conference Poster(s) Conference Talks(s)

Under the guidance of Dr. Fatemeh Ghoreishi, I trained, tested, and optimized a Support Vector Machine (SVM) classification model capable of differentiating a patient with Bipolar Disorder and a patient with Major Depressive Disorder using a blood biomarker dataset. To validate our evaluation of model performance, I suggested using a confusion matrix alongside the ROC AUC score. Additionally, I highlighted the significance of identifying the key biomarkers in our model, allowing us to explore the literature on their biological relevance in distinguishing between Bipolar and Major Depressive Disorders. Our model resulted in a 94% ROC AUC accuracy score, highlighting its usefulness as a tool to aid diagnosis of psychiatric disorders with similar symptoms.

As a research assistant at Beth Israel, I worked with Dr. Carrie Mahoney to assess the role of different neuronal subpopulations in menopause symptom severity using a mice model of menopause. We used mice engineered to express Cre in our neuronal population of choice. The mice undergo an ovariectomy surgery to represent the loss of estrogen in menopause, and then injected with a Cre-dependent excitatory or inhibitory DREADD. I scored mice behavior and sleep videos, and sectioned and immunostained mouse brains, collecting data at both the behavioral and anatomical levels to analyze how the activation of a kisspeptin neuronal subpopulation affects behavioral and sleep responses in menopause. As menopause is an understudied field, the data I collected will contribute to furthering our understanding of the neural mechanisms of menopause and aid in discovering new possible therapeutic targets to alleviate menopause symptoms.

At Moderna, my project was to develop a Luminex assay for a new vaccine, helping the company choose the best vaccine candidate to be taken from preclinical research to the clinic. We used a multiplexed Luminex assay that was best suited for the virus we were developing the assay for, as the virus produces distinct antibodies at once that we wanted to capture at the same time. I worked with my team to test an initial set of antigens in the assay. I ran Luminex assays with antigens against a mouse serum-based control in different dilution ranges to determine which one resulted in a sufficient dynamic range. I found a dynamic range that worked for most of the antigens, but yielded an insufficient response in a few of them. I hypothesized the insufficient range was due to human error, a problem with the control serum, or a problem with the antigens. To troubleshoot this problem, I ran more Luminex assays using the same control as my team and a different control. I observed the same insufficient dynamic range and concluded that these antigens would not work for the assay. I ran experiments on a new set of antigens and analyzed the data, discovering an optimal dynamic range for the new antigens. The team will use this data to optimize and validate the assay so that it can be used to test and select the vaccine with the best immunogenicity to be taken from preclinical studies to clinical trials. Once the vaccine is tested in clinical trials, it will be distributed to those who need it.